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This paper devises a novel technique, namely Squirrel Search Deer Hunting-based deep recurrent neural network (SSDH-based DRNN) for cancer-survival rate prediction using gene expression (GE) data. Initially, the input GE data are transformed using the polynomial kernel data transformation. Then entropy-based Bayesian fuzzy clustering is employed for gene selection. Then, the selected features are strengthened through survival indicators based on time series data features, like simple moving average (SMA) and rate of change. Finally, the survival rate prediction is performed using a deep recurrent neural network (DRNN), in which the training is carried out with squirrel search deer hunting (SSDH). The proposed SSDH algorithm is devised by combining Squirrel Search Algorithm (SSA) and deer hunting optimization algorithm (DHOA). The performance of the proposed methodology is analyzed using Pan-Cancer (PANCAN) dataset with a prediction error of 4.05%, RMSE of 7.58, the accuracy of 90.98%, precision of 90.80%, recall of 92.03% and F1-score of 91.41%. The devised method with higher prediction accuracy and the lower prediction error is employed for the cancer survival prediction of the patients for the cancer prognosis. Besides, it will be helpful for the clinical management of cancer patients.

Nowadays, skin cancer is one of the most dangerous forms of cancer found in humans. There are various types of skin cancer, like basal, melanoma, carcinoma, and the squamous cell from which the melanoma is unpredictable. Thus, skin cancer detection in the early stage is very useful to treat it successfully. Hence, this study introduces a new algorithm called social bat optimisation algorithm for skin cancer detection. Initially, the pre-processing is done for the input image to eliminate the noise and artefacts present in the image. Then, the pre-processed image is fed to the feature extraction step where the features are extracted based on convolutional neural network features, and the local pixel pattern-based texture feature (local PPBTF). Here, the PPBTF is the combination of texture features and pixel pattern-based features in which the equation of PPBTF is modified based on the local binary pattern. Subsequently, the classification is done based on the extracted features using a deep stacked auto-encoder, which is trained by the proposed social bat optimisation. The performance of skin cancer detection based on the proposed model is evaluated based on accuracy, sensitivity, and specificity. The proposed model achieves the maximal accuracy of 93.38%, maximal sensitivity of 95%, and the maximal specificity of 96% for K-fold.

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