Document Details

Document Type : Thesis 
Document Title :
Enhancing Selling Strategy In E-Markets Based Facial Emotion Recognition
تحسين استراتيجية الشراء في التسوق الالكتروني اعتمادًا على التعرف على مشاعر الوجه
 
Subject : Faculty of Computing and Information Technology 
Document Language : Arabic 
Abstract : This thesis investigates the potential of incorporating bio-inspired techniques, specifically facial emotion recognition technology, in recommendation systems. The proposed model, called EmoCat, utilizes deep learning algorithms to analyze facial expressions and classify emotional states. By leveraging bio-inspired approaches like cat swarm optimization, EmoCat aims to provide personalized and engaging product recommendations in e-commerce environments. The study utilizes datasets such as FER-2013 for emotion classification and a product dataset for aligning emotional states with suitable recommendations. The findings emphasize the effectiveness of integrating bio-inspired techniques, like facial emotion recognition, in enhancing recommendation accuracy and customer satisfaction. By considering both customer emotions and bio-inspired approaches, businesses can create recommendation systems that better cater to individual preferences, resulting in improved customer experiences and increased sales. 
Supervisor : Prof. Shahenda Sarhan 
Thesis Type : Master Thesis 
Publishing Year : 1445 AH
2023 AD 
Co-Supervisor : Dr. Souad Baowidan 
Added Date : Friday, December 1, 2023 

Researchers

Researcher Name (Arabic)Researcher Name (English)Researcher TypeDr GradeEmail
عهد مسعود المسعوديAlmasoudi, Ahad MasoudResearcherMaster 

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