An Efficient SecureU Application to Detect Malicious Applications in Social Media Networks

Roshini, A; sai, V.D.Varun; Chowdary, S.Dinesh; Kommineni, Madhuri; Haldorai, Anandakumar

doi:10.1109/icaccs48705.2020.9074169

Cited by 4 publications

(1 citation statement)

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“…However, there remain unresolved inquiries regarding specific aspects that were deliberately concealed [41]. Prolonged utilization can potentially impact various aspects such as sleep patterns, circadian rhythms, and performance in shift work, among other factors [42]. The variability in the conceptualization of sleep and the diverse methodologies employed to assess it across different studies precludes the establishment of definitive conclusions regarding the association between sleep and mental fatigue.…”

Section: Physiological Metricsmentioning

confidence: 99%

Mental State Adaptive Interfaces as a Remedy to the Issue of Long-term, Continuous Human Machine Interaction

Nderitu

2023

JRS

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In order to promote safer and more efficient human-machine interaction, this article advocates for the employment of adaptive systems that account for the user's mental state throughout the duration of lengthy, continuous usage. Perhaps what is needed are adaptive systems that can adjust to the user's mood. The operator's state of mind may be inferred using a combination of operator-independent metrics (for instance, time of day and weather) and behavior (for instance, lane deviation and response time) and physiological (for instance, heart activity and electroencephalography) indicators. Several changes may be made to the dynamic between the operator and the system to mitigate the impacts of the operator's diminished cognitive capacity and preserve the reliability and efficacy of operations. Depending on the specifics of the job at hand and the difficulties that must be overcome, adjustments may be made to factors such as the type of the information presented, the structure of the presentation, the prominence of the stimuli, and the order in which the tasks are performed, frequently using the predictions produced by machine learning.

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Section: Physiological Metricsmentioning

confidence: 99%