Polycystic ovary syndrome detection using neural network.

Polycystic ovary syndrome detection using neural network.

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

Xehetasun bibliografikoak
Egile Nagusiak:	Istiyaq, Tahsin, Jahan, Nusrat, Diptho, Rakib Ahmmed, Anika, Fairuz, Sadakin, Sifat-E
Beste egile batzuk:	Hossain, Muhammad Iqbal
Formatua:	Thesis
Hizkuntza:	English
Argitaratua:	Brac University 2024
Gaiak:	Machine learning KNN algorithm Linear regression analysis Regression analysis Polycystic ovary syndrome
Sarrera elektronikoa:	http://hdl.handle.net/10361/22861

Antzeko izenburuak

Prevalence and associated risk factors of polycystic ovary syndrome in Asian countries
nork: Eathan, Bodron Nesa, et al.
Argitaratua: (2024)

An update on Polycystic Ovary Syndrome (PCOS): diagnosis, risks, etiology, and treatment options revisited
nork: Islam, Hiya, et al.
Argitaratua: (2021)

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

Machine learning based analysis and prediction of crop yield and prices of Aman, Aus and Boro rice
nork: Bhattacharjee, Ruposri, et al.
Argitaratua: (2024)

Weather forecasting using Deep Learning
nork: Fahad, Sheikh Abdul, et al.
Argitaratua: (2023)

Predicting the length of stay in a hospital for a particular disease using machine learning algorithms
nork: Imam, Saif
Argitaratua: (2021)

A review of herbal medicines used in the treatment of PCOS: A breakthrough in modern era
nork: Poly, Taslima Sultana
Argitaratua: (2023)

Performance analysis of machine learning algorithms in resume recommendation systems
nork: Hasan, Ibteaz, et al.
Argitaratua: (2018)

Supervising vehicle using pattern recognition
nork: Lushan, Mihodi Hasan, et al.
Argitaratua: (2018)

Application of CNN based architectures in detection of distracted drivers
nork: Arafin, Irfana, et al.
Argitaratua: (2023)

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

SCAMM: detection and prevention of SQL injection attacks using a machine learning approach
nork: Alam, Auninda, et al.
Argitaratua: (2021)

Flood prediction using ensemble machine learning models
nork: Rahman, Tanvir
Argitaratua: (2024)

Heart disease prediction using techniques of classification in machine learning
nork: Afrose, Sadia, et al.
Argitaratua: (2021)

Machine learning for stress prediction
nork: Salehin, Sherajus, et al.
Argitaratua: (2023)

A thesis on utilizing machine learning models to predict material hardship
nork: Das, Tirtha
Argitaratua: (2024)

Predicting stock market trend from twitter feed and building a framework for Bangladesh
nork: Karim, Shabab, et al.
Argitaratua: (2018)

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

Estimating flood susceptibility of Bangladesh in the future year using machine learning
nork: Alim, Sakib Bin, et al.
Argitaratua: (2021)

Early grade prediction using profile data
nork: Iqbal, Sumaiya, et al.
Argitaratua: (2021)

Predicting a T20 cricket match result while the match is in progress
nork: Munir, Fahad, et al.
Argitaratua: (2015)

Finding ideal geographical location for businesses using machine learning technique
nork: Mahmud, Mir Ibtid, et al.
Argitaratua: (2022)

A novel approach to reduce air pollution through machine learning based PM2.5 prediction
nork: Alif, Omar Farhad, et al.
Argitaratua: (2023)

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

Travel time prediction using machine learning and weather impact on traffic conditions
nork: Deb, Bilash, et al.
Argitaratua: (2018)

Identification of fake news using machine learning in distributed system
nork: Saif, Mehruz, et al.
Argitaratua: (2021)

Supershop management Models: An optimised way to manage the supershop using hyperautomation and machine learning
nork: Ahmed, Shuvro, et al.
Argitaratua: (2023)

A feature selection approach to determine obesity using machine learning method
nork: Antor, Shahidul Alam, et al.
Argitaratua: (2021)

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

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

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

Intelligent assisted living in pregnancy
nork: Zaman, S. M. Tanveer, et al.
Argitaratua: (2022)

Automated fabric color prediction
nork: Muhtashima, Fawzia, et al.
Argitaratua: (2024)

Prediction of bipolar disorder from mental episodes using machine learning approach
nork: Tasmeem, Sumaiya, et al.
Argitaratua: (2023)

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

Data analysis on the Bangladesh share market using Machine-Learning
nork: Sadman, Syed Hassan, et al.
Argitaratua: (2021)

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

Forecasting stock market prices using advanced tools of machine learning
nork: Anwar, Md. Tawhid, et al.
Argitaratua: (2019)

Detecting tweet sentiment and sarcasm on online learning during covid-19
nork: Das, Shishir Kumar, et al.
Argitaratua: (2024)

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