Monkeypox is a zoonosis disease that can spread from animals to people. Squirrels, rats taken from Gambian slums, dormice, various monkey species, and other animals have all shown signs of monkeypox virus infection. Contact with bodily fluids, sores on the skin or on internal mucosal surfaces, like those in the mouth or throat, respiratory droplets, and infected objects can all result in the spread of the disease. As the World Health Organization has warned the entire world against this disease, it is necessary to predict its prevalence in the entire world. This study uses a polynomial neural network model to predict monkeypox prevalence. Data on confirmed monkeypox cases collected from 6 May 2022 to 28 July 2022 are presented here. Based on the data, the prediction will be done using the group method of data handling model. The intensity of the spreading of this disease in the 100 days to come will be predicted in this study. The prediction will be done around the world, especially around the countries of the Asian continent which have been tremendously affected by the said disease.

Automatic facial expression recognition (FER) plays a crucial role in human-computer based applications such as psychiatric treatment, classroom assessment, surveillance systems, and many others. However, automatic FER is challenging in real-time environment. The traditional methods used handcrafted methods for FER but mostly failed to produce superior results in the wild environment. In this regard, a deep learning-based FER approach with minimal parameters is proposed, which gives better results for lab-controlled and wild datasets. The method uses features boosting module with skip connections which help to focus on expression-specific features. The proposed approach is applied to FER-2013 (wild dataset), JAFFE (lab-controlled), and CK+ (lab-controlled) datasets which achieve accuracy of 70.21%, 96.16%, and 96.52%. The observed experimental results demonstrate that the proposed method outperforms the other related research concerning accuracy and time.

In this study, Intuitionistic Fuzzy Consistency Method (IF-FUCOM) and Grey Relation Analysis (GRA) were combined to assess the effects of Bacillus subtilis bacteria on concrete properties, as well as to determine the optimal bacteria concentration and curing day. Three different concentrations of bacteria were added to the mortar mixes, like 103, 105, and 107 cells/ml of water. Mortar samples were left to cure for 7 days, 14 days, and 28 days to evaluate compressive strength, water absorption, crack healing. According to the proposed algorithm, 105 bacteria are the optimal concentration, while 28 days is the ideal curing time.

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