Document Details
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Document Type |
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Thesis |
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Document Title |
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Enhancing Selling Strategy In E-Markets Based Facial Emotion Recognition تحسين استراتيجية الشراء في التسوق الالكتروني اعتمادًا على التعرف على مشاعر الوجه |
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Subject |
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Faculty of Computing and Information Technology |
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Document Language |
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Arabic |
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Abstract |
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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. |
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Supervisor |
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Prof. Shahenda Sarhan |
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Thesis Type |
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Master Thesis |
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Publishing Year |
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1445 AH
2023 AD |
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Co-Supervisor |
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Dr. Souad Baowidan |
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Added Date |
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Friday, December 1, 2023 |
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Researchers
| عهد مسعود المسعودي | Almasoudi, Ahad Masoud | Researcher | Master | |
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