Abstract Event detection in real‐time is applied in diverse domains such as detection of fraudulent activities in commercial transactions, detection of faulty systems in industries, and so forth. Businesses and organizations benefit from the actionable information obtained through various techniques available for anomalous event detection. Real‐time event detection is nowadays handled through streaming data frameworks. Traditional approaches effectively handle event detection in real‐time but with more false positives, thus, resulting in false alarms. In this article, an efficient approach comprising two components, an offline model and an online event detection pipeline, is proposed to achieve minimum mean absolute error (MAE). An offline module is developed to investigate a variety of deep learning models that prove suitable for event detection in real‐time. The experiments conducted with PubNub sensors datasets demonstrate that the long short‐term memory unit of recurrent neural networks is the best suitable model for anomalous event detection. The online pipeline module is built using streaming data frameworks to predict the abnormal peaks. It is revealed through the experimental results that the proposed approach efficiently detects anomalous events in real‐time and also eliminates false positives.

Summary The perpetual increase in social media data due to social distancing policy in practice and the low cost of communication has acquired an interest in rumor detection in social media. During the togetherness of the globalized world in the pandemic situation of Covid‐19, social media applications drive excellent value to the analysts and journalists working in different domains. However, the unmoderated nature of social media users often leads to the spread of rumors due to a variety of opinions on government decisions, which becomes notoriously hard to detect. Also, the users may deny rumors as soon as they are debunked. This research extracts diffused information such as word cloud, hashtags, re‐tweets and so forth and uses it as features. These features are applied in the sliding windows of data streams to detect tweets that may be rumors by validating using credible news sources. To address the challenge of veracity, tweets are classified into rumors and nonrumors. A modified unsupervised VADER sentiment classifier is proposed to further classify rumors tweets into amalgamated, unauthoritative, or exaggerated. It is revealed from the results that the proposed classifier is quite efficient in finding and classifying rumorous tweets that arise in critical situations.

In this paper, a variant of particle swarm optimization (PSO) is presented to handle the problem of stagnation encounters in PSO which may lead to get it trapped in local optima and premature convergence particularly in multimodal problems. The proposed scheme Dynamic-PSO (DPSO) does not disturb the fast convergence characteristics of PSO by keeping the basic concept of PSO unaffected. When particles personal best and swarm's global best position do not improve in successive generation i.e. start stagnating DPSO provides dynamicity to particles externally in such a manner that stagnated particles move towards potentially better unexplored region to maintain diversity as this increases chance to recover from stagnation. By identifying and curing stagnated particles, it also avoids the problems of getting trapped in local optima and premature convergence. We have compared the proposed algorithm DPSO with basic PSO and its widely accepted variants over 24 benchmark functions provided by Black-Box Optimization Benchmarking (BBOB 2013). Results show that the proposed variant performs better in comparison with other peer algorithms.