Performance evaluation of all experiments.

<div><p>Surveillance systems are integral to ensuring public safety by detecting unusual incidents, yet existing methods often struggle with accuracy and robustness. This study introduces an advanced framework for anomaly recognition in surveillance, leveraging deep learning to address t...

وصف كامل

محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Shaista Khanam (21387548) (author)
مؤلفون آخرون:	Muhammad Sharif (7039565) (author), Mudassar Raza (17145050) (author), Waqar Ishaq (21387551) (author), Muhammad Fayyaz (7643474) (author), Seifedine Kadry (8713629) (author)
منشور في:	2025
الموضوعات:	Neuroscience Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified proposed approach achieves net ,&# 8221 leveraging deep learning ensuring public safety employs two dcnns detecting unusual incidents achieve significant improvements enhance feature visibility 2500 selected features study &# 8217 deep learning models study introduces features extracted feature extraction &# 8220 unprecedented 99 substantial leap practical real novel 63 new benchmark layer cnn innovative combination genetic algorithm ga optimizer ga ). framework begins fold cross existing methods established inception current techniques contribution lies anomaly recognition advanced framework
الوسوم:	إضافة وسم لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!

Performance evaluation of all experiments.

مواد مشابهة