FRET is a nonradiative process of energy transfer that is based on the dipole–dipole interactions between molecules that are fluorescent.

Since the end of 2019, computed tomography (CT) images have been used as an important substitute for the time‐consuming Reverse Transcriptase polymerase chain reaction (RT‐PCR) test; a new coronavirus 2019 (COVID‐19) disease has been detected and has quickly spread through many countries across the world. Medical imaging such as computed tomography provides great potential due to growing skepticism toward the sensitivity of RT‐PCR as a screening tool. For this purpose, automated image segmentation is highly desired for a clinical decision aid and disease monitoring. However, there is limited publicly accessible COVID‐19 image knowledge, leading to the overfitting of conventional approaches. To address this issue, the present paper focuses on data augmentation techniques to create synthetic data. Further, a framework has been proposed using WoT and traditional U‐Net with EfficientNet B0 to segment the COVID Radiopedia and Medseg datasets automatically. The framework achieves an F ‐score of 0.96, which is best among state‐of‐the‐art methods. The performance of the proposed framework also computed using Sensitivity, Specificity, and Dice‐coefficient, achieves 84.5%, 93.9%, and 65.0%, respectively. Finally, the proposed work is validated using three quality of service (QoS) parameters such as server latency, response time, and network latency which improves the performance by 8%, 7%, and 10%, respectively.

Convolutional neural networks (CNN) have become a popular choice for image segmentation and classification. Internal body images are obscure in nature with involvement of noise, luminance variation, rotation and blur. Thus optimal choice of features for machine learning model to classify bleeding is still an open problem. CNN is efficient for attribute selection and ensemble learning makes a generalized robust system. Capsule endoscopy is a new technology which enables a gastroenterologist to visualize the entire digestive tract including small bowel to diagnose bleeding, ulcer and polyp. This paper presents a supervised learning ensemble to detect the bleeding in the images of Wireless Capsule Endoscopy. It accurately finds out the best possible combination of attributes required to classify bleeding symptoms in endoscopy images. A careful setting for CNN layer options and optimizer for back propagation after reducing the color palette using minimum variance quantization has shown promising results. Results of testing on public and real dataset has been analyzed. Proposed ensemble is able to achieve 0.95 on the public endoscopy dataset and 0.93 accuracy on the real video dataset. A detailed data analysis has also been incorporated in the study including RGB pixel intensities, distributions of binary classes and various class ratios for training.

This paper compares four prediction methods namely Random Forest Regressor (RFR), SARIMAX, Holt-Winters (H-W), and the Support Vector Regression (SVR) to forecast the total CO2 emission from the paddy crop in India. The major objective of this study is to compare these four models to suggest an effective model to predict the total CO2 emission. Data from 1961 to 2018 has been categorised into two parts: training and test data. The study forecasts total CO2 emission from paddy crop in India from 2019 to 2025. A comparison of mean absolute percentage error (MAPE) and the mean square error (MSE), highlights the differences in accuracy among the four models. The mean absolute percentage error (MAPE) and the mean square error (MSE) for the four methods are:

The novel coronavirus infection (COVID-19) first appeared in Wuhan, China, in December 2019. COVID-19 declared as a global pandemic by the WHO was the most rapidly spreading disease all across the world. India, the second most populated nation in the world, is still fighting it, when coronavirus reached the stage where community transmission takes place at an exponential rate. Therefore, it is crucial to examine the future trends of COVID-19 in India and anticipate how it will affect economic and social growth in a short run. In this paper, a new deep learning framework using CNN and stacked Bi-GRU has been developed for predicting and analyzing the COVID-19 cases in India. The proposed model can predict the next 30 days’ new positive cases, new death cases, recovery rate and containment and health index values with high accuracy. The proposed method is compared against Gaussian process regression (GPR) model on COVID-19 datasets. The experimental result shows that the proposed framework is highly reliable for COVID-19 prediction over the GPR model.