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The continuous rise in skin cancer cases, especially in malignant melanoma, has resulted in a high mortality rate of the affected patients due to late detection. Some challenges affecting the success of skin cancer detection include small datasets or data scarcity problem, noisy data, imbalanced data, inconsistency in image sizes and resolutions, unavailability of data, reliability of labeled data (ground truth), and imbalance of skin cancer datasets. This study presents a novel data augmentation technique based on covariant Synthetic Minority Oversampling TEchnique (SMOTE) to address the data scarcity and class imbalance problem. We propose an improved data augmentation model for effective detection of melanoma skin cancer. Our method is based on data oversampling in a non-linear lower-dimensional embedding manifold for creating synthetic melanoma images. The proposed data augmentation technique is used to generate a new skin melanoma dataset using dermoscopic images from the publicly available P H 2 dataset. The augmented images were used to train the SqueezeNet deep learning model. The experimental results in binary classification scenario show a significant improvement in detection of melanoma with respect to accuracy (92.18%), sensitivity (80.77%), specificity (95.1%), and F1-score (80.84%). We also improved the multi-class classification results in melanoma detection to 89.2% (sensitivity), 96.2% (specificity) for atypical nevus detection, 65.4% (sensitivity), 72.2% (specificity), and for common nevus detection 66% (sensitivity), 77.2% (specificity). The proposed classification framework outperforms some of the state-of-the-art methods in detecting skin melanoma.

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Security breaches have been observed in different dimensions in mobile payment system. The violation of user's privacy is a common phenomenon in mobile payment transactions. This study presents an improved security scheme for a mobile payment system using elliptic curve cryptography over a binary field with International Mobile Equipment Identity to ensure higher security. The scheme uses a payment gateway for registration and maps all input text to elliptic curve points using ASCII values. Payment details are stored on the gateway, which is encrypted but decrypted only with merchant's decryption key. The proposed scheme was evaluated in terms of key size, security strength, computational power, memory capacity, encryption and decryption time and mobile phone battery. The result shows that the scheme provides integrity, confidentiality and privacy. The result also shows that the proposed scheme is time-efficient and computationally inexpensive for resourceconstrained environment like mobile payment system.

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