The Covid‐19 pandemic has emerged as one of the most disquieting worldwide public health emergencies of the 21st century and has thrown into sharp relief, among other factors, the dire need for robust forecasting techniques for disease detection, alleviation as well as prevention. Forecasting has been one of the most powerful statistical methods employed the world over in various disciplines for detecting and analyzing trends and predicting future outcomes based on which timely and mitigating actions can be undertaken. To that end, several statistical methods and machine learning techniques have been harnessed depending upon the analysis desired and the availability of data. Historically speaking, most predictions thus arrived at have been short term and country‐specific in nature. In this work, multimodel machine learning technique is called EAMA for forecasting Covid‐19 related parameters in the long‐term both within India and on a global scale have been proposed. This proposed EAMA hybrid model is well‐suited to predictions based on past and present data. For this study, two datasets from the Ministry of Health & Family Welfare of India and Worldometers, respectively, have been exploited. Using these two datasets, long‐term data predictions for both India and the world have been outlined, and observed that predicted data being very similar to real‐time values. The experiment also conducted for statewise predictions of India and the countrywise predictions across the world and it has been included in the Appendix.

Suspicious pre- and post-activity detection in crowded places is essential as many suspicious activities may be carried out by culprits. Usually, there will be installations of surveillance cameras. These surveillance cameras capture videos or images later investigated by authorities and post-event such suspicious activity would be detected. This leads to high human intervention to detect suspicious activity. However, there are no systems available to protect valuable things from such suspicious incidents. Nowadays machine learning (ML)- and deep learning (DL)-based pre-incident warning alarm systems could be adapted to monitor suspicious activity. Suspicious activity prediction would be based on human gestures and unusual activity detection. Even though some methods based on ML or DL have been proposed, the need for a highly accurate, highly precise, low-false-positive and low-false-negative prediction system can be enhanced by hybrid or enhanced ML- or DL-based systems. This proposed research work has introduced an enhanced convolutional neural network (ECNN)-based suspicious activity detection system. The experiment was carried out and the results were claimed. The results are analyzed with the Statistical Package for the Social Sciences (SPSS) tool. The results showed that the mean accuracy, mean precision, mean false-positive rate, and mean false-negative rate of suspicious activity detections were 97.050%, 96.743%, 2.957%, and 2.927% respectively. This result was also compared with the convolutional neural network (CNN) algorithm. This research work can be applied to enhance the pre-suspicious activity alert security system to avoid risky situations.

To find out precise Fake News Detection on social media, the Decision Tree (DT) machine learning algorithm has to be worked out and also compare textual property accuracy with Support Vector Machine (SVM) machine learning algorithm. Materials and Methods: The analysis was done for fake news detection using machine learning algorithms such as the DT algorithm (N=311) and SVM algorithm (N=311) as a proposed research. Results: The DT algorithm and SVM algorithm’s accuracy for social media fake news were experimented and measured. The DT machine algorithm accuracy measures appears to be 97.67% and it is better than the SVM algorithm accuracy and it appears to be 91.74%. There is a statistically significant difference among the study groups with significance value 0.092 for accuracy and 0.825 for precision for Confidence Interval (CI) 95%. Conclusion: The DT machine algorithm is used in examining whether a piece of news from social media is fake or not with accuracy that appears to be better than the SVM algorithm machine learning algorithm.

Network data traffic is increasing with expanded networks for various applications, with text, image, audio, and video for inevitable needs. Network traffic pattern identification and analysis of traffic of data content are essential for different needs and different scenarios. Many approaches have been followed, both before and after the introduction of machine and deep learning algorithms as intelligence computation. The network traffic analysis is the process of incarcerating traffic of a network and observing it deeply to predict what the manifestation in traffic of the network is. To enhance the quality of service (QoS) of a network, it is important to estimate the network traffic and analyze its accuracy and precision, as well as the false positive and negative rates, with suitable algorithms. This proposed work is coining a new method using an enhanced deep reinforcement learning (EDRL) algorithm to improve network traffic analysis and prediction. The importance of this proposed work is to contribute towards intelligence-based network traffic prediction and solve network management issues. An experiment was carried out to check the accuracy and precision, as well as the false positive and negative parameters with EDRL. Also, convolutional neural network (CNN) machines and deep learning algorithms have been used to predict the different types of network traffic, which are labeled text-based, video-based, and unencrypted and encrypted data traffic. The EDRL algorithm has outperformed with mean Accuracy (97.20%), mean Precision (97.343%), mean false positive (2.657%) and mean false negative (2.527%) than the CNN algorithm.