Melanoma is one of the most severe type of skin cancer caused by the DNA mutations in pigment cells called melanocytes. The current diagnosing methods involves visually examining lesion images which makes it time consuming. This makes it an active area of research which is being pursued using various deep-learning models. However, classification models that are developed using limited labeled training data might have a negative impact on the diagnostic procedure. This study presents a self-supervised learning-based model trained on dermoscopic images for automated melanoma recognition, in which feature extraction is accomplished using unlabeled data, and further fine-tuning is done using labeled data for classification. Additionally, an intermediary step is included for augmentation using Deep Convolution Generative Adversarial Networks (DCGAN) to enhance the number of unlabeled images for the pretext learning step. The trained model improved the accuracy by 4% as compared to just training using the original dataset. The proposed method can be extended to a broader range of applications in the medical area, where there is a real scarcity of data to train models.

Mankind has been looking up at the stars for centuries, wondering what lies in deep space, and if other civilizations like ours exist. Following this, with the significant advancement in the field of cosmology and space missions, there has been an exponential increase in the astronomical data collected by space telescopes to explore the possibility of harboring extraterrestrial life. And there is still no consensus on whether a planet is habitable, potentially habitable or inhabitable. The only habitable planet is Earth, therefore, the hypothesized exoplanets are categorized using Earth as a reference, also known as the “Earth Habitability Index” (EHI). In this regard, a number of additional metrics have been developed to categorize an exoplanet's habitability score, such as the Cobb-Douglas Habitability Score, which is a metric based on the Cobb-Douglas habitability production function (CD-HPF). Many classification-based algorithms have already been developed, but they have limitations, such as the possibility of misleading accuracy scores when applied to highly unbalanced datasets. Recently, some work has also been proposed in anomaly detection using memetic algorithms, which belong to the class of metaheuristic algorithms, but the number of feature sets used is significantly less compared to the ones impacting the habitability score of an exoplanet. In this present research, we are proposing a novel variational auto-encoder algorithm that works on a probability distribution function belonging to the class of anomaly detection and will work on a greater number of features in a significantly larger dataset. The proposed algorithm follows an unsupervised learning approach to detect anomalies in order to determine potentially habitable exoplanets. To validate the approach, the obtained results will be cross-matched with the dataset provided by the Planetary Habitability Laboratory's habitable exoplanet catalog (PHL-HEC).