Naveen Kumar

TL;DR: This study proposes a novel hierarchical supervised-learning approach to increase the likelihood of detecting anomalies by analyzing several user features and then characterizing their collective behavior in a unified manner, which can improve the performance of fake reviewer detection on digital platforms.

TL;DR: The theory of cognitive load is discussed, EEG frequency bands likely to capture the cognitive load are identified, and a methodology to measure cognitive load using EEG power spectrum is proposed and verifies the framework by an experimental study.

TL;DR: A review of the literature dealing with critical aspects of yield modelling is provided in this article, where a review of many topics from simple probabilistic yield models to the incorporation of critical features such as spatial defects and radial yield losses is provided.

TL;DR: Major stakeholders, including those in the user community, providers, vendors and developers, and policy makers need to come together to survey the landscape, identify the issues, and develop policies, regulations, safeguards and industry standards.