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Abnormal behavior, often referred to as mental illness, results from changes in brain function that influence thought patterns, behaviors, and social interactions. Psychologists and psychiatrists typically assess abnormal behavior using three primary criteria: deviance, maladaptation, and personal distress, particularly when these traits persist over long periods.
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Behavioral approaches have often been criticized for ignoring mental processes and focusing solely on observable behavior. However, these approaches provide an optimistic perspective for individuals seeking to change their behaviors. Rather than concentrating on intrinsic personality traits, behavioral approaches suggest that even longstanding habits can be modified by changing the reward contingencies that maintain them.
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LightAnomalyNet: A Lightweight Framework for Efficient Abnormal Behavior Detection.

Abid Mehmood1

  • 1Department of Management Information Systems, College of Business Administration, King Faisal University, Al-Ahsa 31982, Saudi Arabia.

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|December 28, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces LightAnomalyNet, a lightweight framework for detecting anomalies like falls and violence in surveillance videos. It achieves superior accuracy and efficiency compared to existing methods.

Failed At:

2026-06-19T13:39:19.987668+00:00

Keywords:
anomaly detectionbehavior analysisconvolutional neural networkfall detectionsuspicious behavior detectionviolence detection

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