The colossal increase in environmental pollution and degradation, resulting in ecological imbalance, is an eye-catching concern in the contemporary era. Moreover, the proliferation in the development of smart cities across the globe necessitates the emergence of a robust smart waste management system for proper waste segregation based on its biodegradability. The present work investigates a novel approach for waste segregation for its effective recycling and disposal by utilizing a deep learning strategy. The YOLOv3 algorithm has been utilized in the Darknet neural network framework to train a self-made dataset. The network has been trained for 6 object classes (namely: cardboard, glass, metal, paper, plastic and organic waste). Moreover, for comparative assessment, the detection task has also been performed using YOLOv3-tiny to validate the competence of the YOLOv3 algorithm. The experimental results demonstrate that the proposed YOLOv3 methodology yields satisfactory generalization capability for all the classes with a variety of waste items.

The global explosion of the COVID-19 pandemic has created worldwide unprecedented health and economic challenges which stimulated one of the biggest annual migrations globally. In the Indian context, even after proactive decisions taken by the Government, the continual growth of COVID-19 raises questions regarding its extent and severity. The present work utilizes the susceptible-infected-recovered-death (SIRD) compartment model for parameter estimation and fruitful prediction of COVID-19. Further, various optimization techniques such as particle swarm optimization (PSO), gradient (G), pattern search (PS) and their hybrid are employed to solve the considered model. The simulation study endorse the efficiency of PSO (with or without G) and G+PS+G over other techniques for ongoing pandemic assessment. The key parametric values including characteristic time of infection and death and reproduction number have been estimated as 60 days, 67 days and 4.78 respectively by utilizing the optimum results. The model assessed that India has passed its peak duration of COVID-19 with more than 81% recovery and only a 1.59% death rate. The short duration analysis (15 days) of obtained results against reported data validates the effectiveness of the developed models for ongoing pandemic assessment.

Background: The aim of the study was to assess the attitude of the school and college-going students towards online classes. Methods: An online cross-sectional study was conducted on 228 school and college-going students fulfilling inclusion and exclusion criteria selected through purposive sampling methods. A semi-structured online questionnaire consisting of a socio-demographic questionnaire and Attitude towards online classes (ATOC) questionnaire was prepared by the researcher using Google form. The link of the questionnaire was sent to all the selected respondents through WhatsApp messages and emails. The data was analyzed using the IBM SPSS version 20 software. The reliability of the attitude questionnaire was assessed using Cronbach’s alpha test. The association between categorical variables was assessed using Chi-square tests. The comparison between variables was assessed using the students independent t-test. Results: More than half of the respondents (51.32%) were found with a positive attitude towards online classes. There was a significant association found between attitude towards online classes and socio-demographic variables such as age (p<0.05), academic level (p<0.05), and family income (p<0.01). The respondents who attended online classes (p<0.05), have technical knowledge (p<0.01), and got supported by their parents in the study (p<0.05) were found significantly high positive attitudes towards online classes. There was a significant difference found in the attitude of the respondents who faced psychological disturbances such as a decline in attention-concentration (p<0.05), irritation-anger (p<0.01), and tension (p<0.05) due to online classes. Conclusions: Although, online classes are more beneficial for the students and teachers in their academic activities during the lockdown period due to the COVID-19 pandemic but it can’t take place of traditional face-to-face classes.