Machine learning-based approach on predicting online shopping addiction using EEG signals

Machine learning-based approach on predicting online shopping addiction using EEG signals

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.

Bibliographische Detailangaben
Hauptverfasser:	Nawer, Nafisa, Jahan, Nazia, Fuwad, Md. Mubtasim, Bhuiyan, Mehedi Hasan, Kabir, Imtiaz
Weitere Verfasser:	Chakrabarty, Amitabha
Format:	Abschlussarbeit
Sprache:	English
Veröffentlicht:	Brac University 2023
Schlagworte:	Shopping addiction Machine learning Electroencephalography Support Vector machine Gated recurrent unit Decision tree Random forest Brain mapping Artificial intelligence > Engineering applications
Online Zugang:	http://hdl.handle.net/10361/21822

Ähnliche Einträge

Detection of mind wandering using EEG signals
von: Tasika, Nadia Jebin, et al.
Veröffentlicht: (2020)

Credit card fraud detection using machine learning techniques
von: Kabir, Tasmia, et al.
Veröffentlicht: (2021)

Effectiveness of data mining in predicting heart diseases
von: Afrin, Shahria, et al.
Veröffentlicht: (2018)

A supervised machine learning approach to predict vulnerability to drug addiction
von: Faisal, Fahim, et al.
Veröffentlicht: (2020)

A counseling system to predict the study path for freshmen
von: Usha, Rowshni Tasneem, et al.
Veröffentlicht: (2019)

Healing Oasis: De-addiction center for drug addicts
von: Farnaz, Afrida
Veröffentlicht: (2023)

Predicting brain age from EEG signals using machine learning and neural network
von: Pratanu, Abul Mushfique Muslah, et al.
Veröffentlicht: (2023)

A machine learning approach to analyze and predict rainfall in different regions of Bangladesh
von: Rahee, Arnob, et al.
Veröffentlicht: (2021)

Where will you setup your business next?: a machine learning approach to suggest ideal geographical location for new restaurant establishment
von: Shihab, Ibne Farabi, et al.
Veröffentlicht: (2018)

Heart disease prediction system
von: Amit, Tayab Al Azad, et al.
Veröffentlicht: (2022)

A machine learning approach to predict young voter enthusiasm based on non-political factors
von: Rahman, Md. Nowroz Junaed, et al.
Veröffentlicht: (2021)

Predicting stock market movement using sentiment analysis of twitter feed
von: Chakraborty, Pranjal, et al.
Veröffentlicht: (2017)

Diagnosis of cervical cancer using effective hybrid model in Bangladesh
von: Saad-Ul-Kabir, Syed, et al.
Veröffentlicht: (2021)

Detection of epileptic seizure using Support Vector Machine Classifier - extracted features from EEG signals
von: Amiz, Asef Hassan, et al.
Veröffentlicht: (2021)

Cardiovascular disease prediction model using Machine Learning Algorithm
von: Arefin Mirdha, MD Shamsul
Veröffentlicht: (2023)

Parking data analysis
von: Ahamed, Kasfi, et al.
Veröffentlicht: (2023)

A hybrid rumor detection model derived from a comparative study of supervised approaches
von: Aothoi, Mehzabin Sadat, et al.
Veröffentlicht: (2023)

Bang-lish sentiment classification using deep learning
von: Saleheen, Abrar, et al.
Veröffentlicht: (2023)

Smartphone addiction in children: How are parents responsible for it?
von: Hossen, Md Sohrab
Veröffentlicht: (2023)

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

Performance analysis of machine learning classi ers for detecting PE malware
von: Azmee, ABM.Adnan, et al.
Veröffentlicht: (2020)

Amazon product reviews sentiment analysis using supervised learning algorithms
von: Hawladar, Mohibullah, et al.
Veröffentlicht: (2021)

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

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

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

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

Emotion detection using EEG signals
von: Hossain, Mohammad Adnan
Veröffentlicht: (2021)

A machine learning approach to predict movie box-office success
von: Quader, Nahid, et al.
Veröffentlicht: (2018)

Detecting Adverse Drug Reaction (ADR) with data mining and predicting its intensity with machine learning
von: Hussain, Nadib, et al.
Veröffentlicht: (2018)

An introduction to support vector machines : and other kernel-based learning methods /
von: Cristianini, Nello
Veröffentlicht: (2000)

Detecting online recruitment fraud by using machine learning
von: Ghosh, Gitanjali, et al.
Veröffentlicht: (2021)

A supervised learning approach by machine learning and deep learning algorithms to predict type II DM risk
von: Farabe, Abdullah Al, et al.
Veröffentlicht: (2021)

Pattern recognition with Quantum Support Vector Machine(QSVM) on near term quantum processors.
von: Ahmed, Sajjad
Veröffentlicht: (2019)

Finding suitable locations for live campaigns using different machine learning techniques
von: Qudar, Mohiuddin Md. Abdul, et al.
Veröffentlicht: (2018)

Fighting depression: psychological approaches among Bangladeshi university students
von: Sen, Sanjib Kumar, et al.
Veröffentlicht: (2022)

Yield prediction for precision agriculture using extreme gradient boosting and support vector regression
von: Ahmed, Md. Sabbir, et al.
Veröffentlicht: (2021)

Intrusion of Malware (DDoS) detection in IoT devices using Machine Learning on Cyberspace
von: Al Amin, Istiak, et al.
Veröffentlicht: (2022)

A machine learning approach to detect depression and anxiety using supervised learning
von: Ullas, Md Tahmidur Rahman, et al.
Veröffentlicht: (2019)

Sentimental analysis of food review contents to improve food culture and the quality of virtual content using natural language processing and machine learning
von: Sarkar, Ripa, et al.
Veröffentlicht: (2024)

Machine learning for stress prediction
von: Salehin, Sherajus, et al.
Veröffentlicht: (2023)