A supervised machine learning approach to predict vulnerability to drug addiction

A supervised machine learning approach to predict vulnerability to drug addiction

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019.

Bibliografski detalji
Glavni autori:	Faisal, Fahim, Shahriar, Arif, Mahmud, Sohan Uddin, Shuvo, Rakibul Alam
Daljnji autori:	Chakrabarty, Amitabha
Format:	Disertacija
Jezik:	English
Izdano:	Brac University 2020
Teme:	Primary data mRMR Deep beilief nerwork Reliability Vulnerability to addiction Neural network Random forest Neural networks (Computer science) Machine learning
Online pristup:	http://hdl.handle.net/10361/13779

Similar Items

Handwritten character recognition and prediction of age, gender and handedness using machine learning
od: Al Emran, Md., i dr.
Izdano: (2018)

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

Machine learning-based approach on predicting online shopping addiction using EEG signals
od: Nawer, Nafisa, i dr.
Izdano: (2023)

Heart disease prediction system
od: Amit, Tayab Al Azad, i dr.
Izdano: (2022)

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

Best feature selection and data visualization for breast cancer prediction
od: Hemel, Tanjim Ahmed, i dr.
Izdano: (2020)

Prediction of coronary heart diseases using supervised machine learning algorithms
od: Salam, Nazia Binte, i dr.
Izdano: (2023)

Comparative analysis and implementation of credit risk prediction through distinct machine learning models
od: Turjo, Aquib Abtahi, i dr.
Izdano: (2021)

Comparative data analysis of a PV module system considering weather parameters
od: Ahmed, Nafiz, i dr.
Izdano: (2021)

SCAMM: detection and prevention of SQL injection attacks using a machine learning approach
od: Alam, Auninda, i dr.
Izdano: (2021)

Efficient image processing and machine learning approach for predicting retinal diseases
od: Hasib, Mehadi Hasan, i dr.
Izdano: (2021)

Estimating flood susceptibility of Bangladesh in the future year using machine learning
od: Alim, Sakib Bin, i dr.
Izdano: (2021)

Prediction of acute lymphoid leukemia using Privacy Preserving Neural Network
od: Khilji, Ishfaque Qamar, i dr.
Izdano: (2021)

Forecasting stock market prices using advanced tools of machine learning
od: Anwar, Md. Tawhid, i dr.
Izdano: (2019)

Unleashing potential: a data-driven exploration of identifying player potentialities through advanced analytics in sports
od: Bhowmik, Prashanta, i dr.
Izdano: (2024)

Early detection of breast cancer using machine learning
od: Fuad, Wasi Mohammad
Izdano: (2019)

মাদক থেকে দূরে থাকতে হলে
od: দৈনিক প্রথম আলো
Izdano: (2013)

Automated fabric color prediction
od: Muhtashima, Fawzia, i dr.
Izdano: (2024)

Prottoy Healing Sanctuary : psychiatry and de-addiction rehabilitation center Narsingdhi, Bangladesh
od: Showrab, Shahriaz Alam
Izdano: (2019)

Intrusion of Malware (DDoS) detection in IoT devices using Machine Learning on Cyberspace
od: Al Amin, Istiak, i dr.
Izdano: (2022)

A voice signal based gender prediction model using random forest classifier
od: Ahmed, Saif, i dr.
Izdano: (2018)

Fighting depression: psychological approaches among Bangladeshi university students
od: Sen, Sanjib Kumar, i dr.
Izdano: (2022)

Learning a deep neural network for predicting phishing website
od: Das, Robat, i dr.
Izdano: (2019)

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

Crime scene prediction by detecting threatening objects using convolutional neural network
od: Nakib, Mohammad, i dr.
Izdano: (2017)

A color vision approach considering Reflection Co efficient based on Autoencoder techniques using deep neural networks
od: Mahmud, Shakib Izaz, i dr.
Izdano: (2022)

Spinal Cord Injured (SCI) patients Length of Stay (LOS) prediction based on admission data
od: Mazhar, Tabib Ibne, i dr.
Izdano: (2018)

A supervised learning approach by machine learning and deep learning algorithms to predict type II DM risk
od: Farabe, Abdullah Al, i dr.
Izdano: (2021)

A counseling system to predict the study path for freshmen
od: Usha, Rowshni Tasneem, i dr.
Izdano: (2019)

Semantic text extraction from CAPTCHA using Neural Networking
od: Chowdhury, Iftekhar Kabir, i dr.
Izdano: (2022)

An Analysis on Bengali handwritten conjunct character recognition and prediction
od: Munawar, Maazin, i dr.
Izdano: (2021)

Brain hemorrhage detection using hybrid machine learning algorithm
od: Iqbal, Khondoker Nazia, i dr.
Izdano: (2022)

Analysis of malware prediction based on infection rate using machine learning techniques
od: Zawad, Safir, i dr.
Izdano: (2020)

Addressing vulnerabilities in communities facing infectious disease threats: A need for social science-driven assessments
od: Osborne, Jacob, i dr.
Izdano: (2022)

A color vision approach based on the autoencoder technique and deep neural networks for reconstructing color images under various lighting conditions
od: Faisal, Asm, i dr.
Izdano: (2024)

Predicting stock market movement using sentiment analysis of twitter feed
od: Chakraborty, Pranjal, i dr.
Izdano: (2017)

An introduction to neural networks /
od: Anderson, James A.
Izdano: (1995)

Modelling option prices using neural networks
od: Nasim, Ahmed Zohair, i dr.
Izdano: (2020)

A machine learning approach to analyze and predict rainfall in different regions of Bangladesh
od: Rahee, Arnob, i dr.
Izdano: (2021)

Evaluating vulnerability and resilience in the face of earthquake and fire risks
od: Manami, Nishat Tasnim
Izdano: (2024)