Recommendation system for mood stabilization using content recommendation
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.
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Brac University
2024
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| Online toegang: | http://hdl.handle.net/10361/22797 |
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10361-227972024-05-13T21:01:28Z Recommendation system for mood stabilization using content recommendation Al-Wakil, Kazi Md. Rahman, Rifai Nawal, Nafisa Meem, Sababa Rahman Rashid, Sajid Rabiul Alam, Dr. Md. Golam Rahman, Mr. Rafeed Department of Computer Science and Engineering, Brac University Emotion classification Electroencephalograms (EEGs) Content recommendation Mood stabilization Convolutional Neural Network (CNN) Long Short-Term Memory (LSTM) Analytic Hierarchy Process (AHP) Text Classification Pearson correlation Artificial intelligence. Computational intelligence. Computer simulation. User interfaces (Computer systems). This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 63-66). In the era of accelerating technological development, society is confronted with the paradoxical situation of making technological advancements while experiencing a decline in mental health. The importance of mental health seems to be declining significantly. The impact of our daily content intake on emotional well-being is clearly visible. For instance, while a melancholic song can make a person feel sad, an inspirational movie can charge a person’s spirit to come up stronger. Hence we intend to employ this concept to propose a system designed to recommend “Feel Good” YouTube videos with the aim of stabilizing an individual’s mood when it wavers or becomes low. To do this efficiently, we worked on the SEED Dataset, which is composed of EEG signals and Eye Movement data. We implemented a multifaceted approach, including the extraction of Differential Entropy Features, Wavelet Transform, Shannon Entropy features and Eye movement features. These were further harnessed by Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) networks to ensure accurate emotion classification. A thorough evaluation of these two deep learning models in the context of emotion classification is presented by focusing on their relevant merits and demerits. Based on the comparisons it is found that CNN is the most suited for our study with an accuracy of 93.01%. Once a mood classification is achieved, our proposed system will curate and suggest trending “Feel Good” content. To tackle this, we implemented a recommendation system based on the fusion of two prevalent techniques. Initially, text classification was employed to extract the emotion associated with the video and later, Pearson Correlation was utilized to obtain accurate correlation between the contents of the videos based on their corresponding ratings from viewers. Furthermore, concepts of Analytic Hierarchy Process (AHP) have been implemented to come up with an efficient algorithm which works in stabilizing an individual’s mood gradually. In essence, our innovative system encompasses two primary objectives: the detection of an individual’s emotional state through EEG signal analysis and the subsequent stabilization of their mood through targeted content recommendations. By combining these components, we envision a tool that not only comprehends the user’s emotional well-being but actively contributes to its enhancement. Kazi Md. Al-Wakil Rifai Rahman Nafisa Nawal Sababa Rahman Meem Sajid Rashid B.Sc. in Computer Science and Engineering 2024-05-13T04:21:08Z 2024-05-13T04:21:08Z 2023 2023-09 Thesis ID: 23341073 ID: 19201013 ID: 20101353 ID: 23341074 ID: 20101163 http://hdl.handle.net/10361/22797 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 66 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Emotion classification Electroencephalograms (EEGs) Content recommendation Mood stabilization Convolutional Neural Network (CNN) Long Short-Term Memory (LSTM) Analytic Hierarchy Process (AHP) Text Classification Pearson correlation Artificial intelligence. Computational intelligence. Computer simulation. User interfaces (Computer systems). |
| spellingShingle |
Emotion classification Electroencephalograms (EEGs) Content recommendation Mood stabilization Convolutional Neural Network (CNN) Long Short-Term Memory (LSTM) Analytic Hierarchy Process (AHP) Text Classification Pearson correlation Artificial intelligence. Computational intelligence. Computer simulation. User interfaces (Computer systems). Al-Wakil, Kazi Md. Rahman, Rifai Nawal, Nafisa Meem, Sababa Rahman Rashid, Sajid Recommendation system for mood stabilization using content recommendation |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023. |
| author2 |
Rabiul Alam, Dr. Md. Golam |
| author_facet |
Rabiul Alam, Dr. Md. Golam Al-Wakil, Kazi Md. Rahman, Rifai Nawal, Nafisa Meem, Sababa Rahman Rashid, Sajid |
| format |
Thesis |
| author |
Al-Wakil, Kazi Md. Rahman, Rifai Nawal, Nafisa Meem, Sababa Rahman Rashid, Sajid |
| author_sort |
Al-Wakil, Kazi Md. |
| title |
Recommendation system for mood stabilization using content recommendation |
| title_short |
Recommendation system for mood stabilization using content recommendation |
| title_full |
Recommendation system for mood stabilization using content recommendation |
| title_fullStr |
Recommendation system for mood stabilization using content recommendation |
| title_full_unstemmed |
Recommendation system for mood stabilization using content recommendation |
| title_sort |
recommendation system for mood stabilization using content recommendation |
| publisher |
Brac University |
| publishDate |
2024 |
| url |
http://hdl.handle.net/10361/22797 |
| work_keys_str_mv |
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