Predicting effectiveness of marketing through analyzing emotional context in advertisement using deep learning

Predicting effectiveness of marketing through analyzing emotional context in advertisement using deep learning

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020.

Xehetasun bibliografikoak
Egile Nagusiak:	Arafat, Sheikh Mohammad, Islam, Rifatul, Rafi, Ishraque Arefin, Islam, Md. Rashedul
Beste egile batzuk:	Alam, Md. Golam Rabiul
Formatua:	Thesis
Hizkuntza:	en_US
Argitaratua:	Brac University 2021
Gaiak:	Effectiveness of Marketing Emotional States Deep Learning Supervised Machine Learning LSTM-RNN MFCC
Sarrera elektronikoa:	http://hdl.handle.net/10361/14468

Antzeko izenburuak

Predicting crime using deep learning
nork: Shihab, Muhammad Nafees, et al.
Argitaratua: (2024)

Bangla speech recognition using 1D CNN and LSTM with different dimension reduction techniques
nork: Nirjhor, S M Mahsanul Islam, et al.
Argitaratua: (2019)

RED-LSTM: Real time emotion detection using LSTM
nork: Labiba, Mansura Rahman, et al.
Argitaratua: (2023)

Speech emotion detection using supervised, unsupervised and feature selection algorithms
nork: Rifat, Abu Nuraiya Mahfuza Yesmin, et al.
Argitaratua: (2019)

Human sentiment analysis using natural language processing
nork: Shamya, Md. Shamiul Haque Khan, et al.
Argitaratua: (2024)

Analyzing area-wise air pollution level using machine learning for a better future
nork: Sihan, Sk. Atik Tajwar, et al.
Argitaratua: (2021)

Performance analysis of deep learning algorithms for Intrusion Detection System(IDS) of IoT Security
nork: Fiha, Yeamin Jahan, et al.
Argitaratua: (2022)

Stock Market Price movement prediction using RNN and Point-weight Sentiment
nork: Uddin, S. M. Rageeb Noor, et al.
Argitaratua: (2022)

Deep learning based predictive analytics for decentralized content caching in hierarchical edge networks
nork: Chakraborty, Dhruba, et al.
Argitaratua: (2022)

A texture based industrial fault diagnosis model using Gammatone Filter Bank and transfer learning
nork: Morshed, Atique, et al.
Argitaratua: (2021)

Learning a deep neural network for predicting phishing website
nork: Das, Robat, et al.
Argitaratua: (2019)

Dhaka Stock Exchange stock price prediction using Machine Learning and Deep Learning Models
nork: Barua, Debalina
Argitaratua: (2023)

Multimodal emotion recognition from Speech and text using heterogeneous ensemble techniques
nork: Rafidul Islam, Sheikh Md, et al.
Argitaratua: (2022)

Early threat warning via speech and emotion recognition from voice calls
nork: Ishtiak, Ifaz, et al.
Argitaratua: (2019)

Recognition of Bangladeshi sign language from 2D videos using openpose and LSTM based RNN
nork: Dewanjee, Tanmoy, et al.
Argitaratua: (2021)

Predicting regional accents of Bengali language using deep learning
nork: Ibrahim, Md. Abu, et al.
Argitaratua: (2022)

A conventional & deep learning strategy for analyzing & detecting Bengali fake news in online medium
nork: Ahmed, Istiak, et al.
Argitaratua: (2023)

Analysis of financial data on the time series using data from the stock market
nork: Shachcha, Ifad Bhuiyan, et al.
Argitaratua: (2022)

Mental health analysis of cancer-diagnosed patients with the lowest survival rate and their caregivers
nork: Orchi, Irin Hoque, et al.
Argitaratua: (2023)

Hybrid recommendation system of intelligent captioning using deep learning networks
nork: Wasi, Ahamed Al, et al.
Argitaratua: (2024)

Classification of respiratory diseases and COVID-19 from respiratory and cough sound using deep learning techniques
nork: Ahasan, Md. Mubtasim, et al.
Argitaratua: (2022)

Electroencephalogram based Emotion Recognition with Graph Convolutional Network Model
nork: Islam, Md. Tahmidul, et al.
Argitaratua: (2023)

Emotional intelligence : why it can matter more than IQ /
nork: Goleman, Daniel
Argitaratua: (1996)

Behavior change analysis due to violent video gaming using deep learning models
nork: Rahman, Akhlak Ur, et al.
Argitaratua: (2024)

Emotional intelligence /
nork: Goleman, Daniel
Argitaratua: (1995)

Emotion in interaction
nork: Peräkylä, Anssi

Cryptocurrency price prediction and forecasting using machine learning algorithm and long- short term memory mapping
nork: Zaman, Md. Sayeed Ibne, et al.
Argitaratua: (2022)

Emotion detection for Bangla language
nork: Rushan, Rowshan Rahman, et al.
Argitaratua: (2024)

Recognizing sentimental emotions in text by using Machine Learning
nork: Bushra, Tabassum Khan, et al.
Argitaratua: (2023)

TinyML for emotion detection in voice signals: evaluating and proposing algorithms for IoT wearable devices
nork: Ahmed, Hasibul Hasan, et al.
Argitaratua: (2024)

Breast cancer prediction using different machine learning models
nork: Khandker Al- Muhaimin, et al.
Argitaratua: (2020)

Parent’s perception and practices on socio-emotional development of 0-3 years old children
nork: Begum, Iffat Ara
Argitaratua: (2023)

Emotional discipline : the power to choose how you feel : 5 life changing steps to feeling better every day /
nork: Manz, Charles C.
Argitaratua: (2003)

Study on human emotion based on facial expressions through human machine communication
nork: Murshed, Tahmin Aysha, et al.
Argitaratua: (2021)

Exploring cognitive load and emotional states for the visually impaired
nork: Afroz, Sharmin, et al.
Argitaratua: (2019)

Automated image caption generator in Bangla using multimodal learning
nork: Rodoshi, Mashiat Hasin, et al.
Argitaratua: (2023)

Demand forecasting on supply chain using ML and NN
nork: Hridi, Naoshin Anzum, et al.
Argitaratua: (2023)

A deep learning approach to integrate human-level understanding in a Chatbot
nork: Al Mamun, Amirul Islam, et al.
Argitaratua: (2021)

Exploring the perceptions of parents on playful parenting and preschool children’s socio-emotional well-being
nork: Zahan, Sharmin
Argitaratua: (2024)

Deep learning approaches to EEG and fMRI data: a comparative study for sleep stage classification
nork: Tanvir, Farhan, et al.
Argitaratua: (2024)