We have recently been witnessing that our society is starting to heal from the impacts of COVID-19. The economic, social and cultural impacts of a pandemic cannot be ignored and we should be properly equipped to deal with similar situations in future. Recently, Monkeypox has been concerning the international health community with its lethal impacts for a probable pandemic. In such situations, having appropriate protocols and methodologies to deal with the outbreak efficiently is of paramount interest to the world. Early diagnosis and treatment stand as the only viable option to tackle such problems. To this end, in this paper, we propose an ensemble learning-based framework to detect the presence of the Monkeypox virus from skin lesion images. We first consider three pre-trained base learners, namely Inception V3, Xception and DenseNet169 to fine-tune on a target Monkeypox dataset. Further, we extract probabilities from these deep models to feed into the ensemble framework. To combine the outcomes, we propose a Beta function-based normalization scheme of probabilities to learn an efficient aggregation of complementary information obtained from the base learners followed by the sum rule-based ensemble. The framework is extensively evaluated on a publicly available Monkeypox skin lesion dataset using a five-fold cross-validation setup to evaluate its effectiveness. The model achieves an average of 93.39%, 88.91%, 96.78% and 92.35% accuracy, precision, recall and F1 scores, respectively. The supporting source codes are presented in https://github.com/BihanBanerjee/MonkeyPox.

Exomoons have so far eluded ongoing searches. Several studies have exploited transit and transit timing variations and high-resolution spectroscopy to identify potential exomoon candidates. One method of detecting and confirming these exomoons is to search for signals of planet-moon interactions. In this work, we present the first radio observations of the exomoon candidate system WASP 69b. Based on the detection of alkali metals in the transmission spectra of WASP-69b, it was deduced that the system might be hosting an exomoon. WASP 69b is also one of the exoplanet systems that will be observed as part of JWST cycle-1 GTO. This makes the system an excellent target to observe and follow up. We observed the system for 32 hrs at 150 MHz and 218 MHz using the upgraded Giant Metrewave Radio Telescope (uGMRT). Though we do not detect radio emission from the systems, we place strong 3σ upper limits of 3.3 mJy at 150 MHz and 0.9 mJy at 218 MHz. We then use these upper limits to estimate the maximum mass loss from the exomoon candidate.

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