Muhammad Ibrahim

12 papers Misc 1Journal 9Unranked 1
YearRankTypeTitle / Venue / Authors
2025 J jnl
A data-driven approach for predicting crime occurrence using machine learning models.
Int. J. Data Sci. Anal.
Faisal Tareque Shohan, Abu Ubaida Akash, Muhammad Ibrahim, Mohammad Shafiul Alam
2023 J jnl
Transformer-Based Deep Learning Model for Stock Price Prediction: A Case Study on Bangladesh Stock Market.
Int. J. Comput. Intell. Appl.
Tashreef Muhammad, Anika Bintee Aftab, Muhammad Ibrahim, Md. Mainul Ahsan, Maishameem Meherin Muhu, Shahidul Islam Khan, Mohammad Shafiul Alam
2022 J jnl
Crime Prediction using Machine Learning with a Novel Crime Dataset.
CoRR
Faisal Tareque Shohan, Abu Ubaida Akash, Muhammad Ibrahim, Mohammad Shafiul Alam
2022 J jnl
Transformer-Based Deep Learning Model for Stock Price Prediction: A Case Study on Bangladesh Stock Market.
CoRR
Tashreef Muhammad, Anika Bintee Aftab, Md. Mainul Ahsan, Maishameem Meherin Muhu, Muhammad Ibrahim, Shahidul Islam Khan, Mohammad Shafiul Alam
2022 J jnl
Understanding Bias and Variance of Learning-to-Rank Algorithms: An Empirical Framework.
Appl. Artif. Intell.
Muhammad Ibrahim
2020 J jnl
An empirical comparison of random forest-based and other learning-to-rank algorithms.
Pattern Anal. Appl.
Muhammad Ibrahim
2019 J jnl
Reducing correlation of random forest-based learning-to-rank algorithms using subsample size.
Comput. Intell.
Muhammad Ibrahim
2017 J jnl
Scalability and Performance of Random Forest based Learning-to-Rank for Information Retrieval.
SIGIR Forum
Muhammad Ibrahim
2016 J jnl
Comparing Pointwise and Listwise Objective Functions for Random-Forest-Based Learning-to-Rank.
ACM Trans. Inf. Syst.
Muhammad Ibrahim, Mark James Carman
2016 — —
Scalability and Performance of Random Forest based Learning-to-Rank for Information Retrieval.
Muhammad Ibrahim
2014 Misc conf
Improving Scalability and Performance of Random Forest Based Learning-to-Rank Algorithms by Aggressive Subsampling.
AusDM
Muhammad Ibrahim, Mark J. Carman
2014 — conf
Undersampling Techniques to Re-balance Training Data for Large Scale Learning-to-Rank.
AIRS
Muhammad Ibrahim, Mark James Carman