The influence of neuromarketing: machine learning based empirical analysis

The influence of neuromarketing: machine learning based empirical analysis

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.

Bibliografiske detaljer
Main Authors:	Muktadir, MD. Abdullah Al, Malek, Shamsul, Hossain, Zakia
Andre forfattere:	Reza,Md Tanzim
Format:	Thesis
Sprog:	English
Udgivet:	Brac University 2024
Fag:	Neuromarketing Long Short-Term Memory model Machine learning Machine learning.
Online adgang:	http://hdl.handle.net/10361/24048

Lignende værker

An android application to predict human activity using a deep learning LSTM model
af: Sikder, Debabrata
Udgivet: (2024)

Predicting temperature of major cities using machine learning and deep learning
af: Jaharabi, Wasiou, et al.
Udgivet: (2024)

Implementation of machine learning to estimate the air pollutants such as Carbon dioxide, methane, nitrous oxide and total greenhouse gas emissions in Bangladesh
af: Biswas, Sunanda, et al.
Udgivet: (2022)

Economic crisis prediction due to pandemic outbreak using machine learning
af: Ahmed, Tanvir, et al.
Udgivet: (2021)

Conversion of Bengali speech to text using long short-term memory(LSTM)
af: Chowdhury, Mohammad Fahim, et al.
Udgivet: (2021)

Modelling and forecasting energy demand of Bangladesh using AI based algorithms
af: Mahmud, Booshra Nazifa, et al.
Udgivet: (2019)

Share market forecasting with LSTM neural network and sentimental trend prediction
af: Rony, Ismail Hossain, et al.
Udgivet: (2020)

Environmental monitoring with the help of Internet of Things (loT) and machine learning
af: Alam, Md. Sakirul, et al.
Udgivet: (2021)

Economic crisis prediction due to pandemic outbreak using machine learning
af: Ahmed, Tanvir, et al.
Udgivet: (2021)

Utilizing machine learning to project the nancial outcomes of reconnecting with potential customers of the same industry
af: Hossain, Shoumik, et al.
Udgivet: (2022)

Sentiment analysis to determine employee job satisfaction using machine learning techniques
af: Mouli, Nazifa, et al.
Udgivet: (2023)

Leveraging sequential deep learning models for detecting multitude of human action categories
af: Pranta, Kazi Al Refat, et al.
Udgivet: (2024)

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

Rating detection by reviews using ML and NLP towards mobile phone recommendation
af: Sarker, Md Wafi, et al.
Udgivet: (2023)

Consumer behaviour analysis using EEG signals for Neuromarketing Application
af: Amin, Chowdhury Rabith, et al.
Udgivet: (2020)

Empowering mobile network planning through deep learning: a path to democratization
af: Nabi, Syed Tauhidun
Udgivet: (2024)

LSTM based content prediction for edge caching using federated learning approach
af: Mazumder, Shafkat Ahmed, et al.
Udgivet: (2021)

Forecasting market movement in Dhaka Stock Exchange: LSTM vs. ARIMA
af: Chakraborty, Pranjal
Udgivet: (2019)

A deep dive into node-level analysis with fusion RNN model for smart LTE network monitoring
af: Islam, Md Rashidul
Udgivet: (2024)

Fraud detection in E-commerce using natural language processing
af: Kabir, Iftekhar, et al.
Udgivet: (2023)

Heart disease prediction using machine learning algorithms
af: Ali, Musfiq, et al.
Udgivet: (2019)

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

Implementing machine learning techniques to forecast floods In Bangladesh based on historical data
af: Toufique, S.M, et al.
Udgivet: (2024)

Comparative analysis between machine learning algorithms in efficiency of Coronary Heart Disease (CHD) prediction
af: Oishi, Fayza Rezwana, et al.
Udgivet: (2020)

Analysis of the impact of online education using EEG signals and machine learning algorithms
af: Hoque, Ehsanul, et al.
Udgivet: (2021)

Public vehicle ETA system using machine learning
af: Kabir, Md. Miraz, et al.
Udgivet: (2018)

Predicting depression in Bangladeshi undergraduates using machine learning
af: Choudhury, Ahnaf Atef, et al.
Udgivet: (2019)

Crop prediction based on geographical and climatic data using machine learning and deep learning
af: Alif, Al Amin, et al.
Udgivet: (2019)

Alumni opportunities using machine learning
af: Siaum, Arshif Sharlimin, et al.
Udgivet: (2019)

Machine learning as an indicator for breast cancer prediction
af: Shadman, Tahsin Mohammed, et al.
Udgivet: (2019)

Statistical analysis of network data flows and predictions using statistical and machine learning regression models
af: Boateng, Albert, et al.
Udgivet: (2024)

Optical flow based violence detection from video footage using hybrid MobileNet and Bi-LSTM
af: Haque, Tashfia, et al.
Udgivet: (2024)

Flood prediction using ensemble machine learning models
af: Rahman, Tanvir
Udgivet: (2024)

Detecting propagandistic poster title: a machine learning approach
af: Mahmood, Riaz, et al.
Udgivet: (2024)

Machine Learning /
af: Mitchell, Tom M. (Tom Michael), 1951-
Udgivet: (1997)

Classification and Explanation of Different Internet of Things (IoT) Network Attacks using Machine Learning, Deep Learning and XAI
af: Tasnim, Anika, et al.
Udgivet: (2022)

Application of machine learning techniques on the context of livestock analysis
af: Islam, Md. Shajedul, et al.
Udgivet: (2018)

Effects of warranty on purchase decision of electronic products: an empirical research from the perspective of Bangladesh
af: Ullah, G. M. Shafayet, et al.
Udgivet: (2016)

Comparative analysis and implementation of credit risk prediction through distinct machine learning models
af: Turjo, Aquib Abtahi, et al.
Udgivet: (2021)

N.P.K. based crop suggesting model
af: Iftekher, Asif, et al.
Udgivet: (2020)