Dr. Muhammad Ibrahim

Muhammad Ibrahim and Manzur Murshed "From Tf-Idf to Learning-to-Rank: An Overview." Handbook of Research on Innovations in Information Retrieval, Analysis, and Management (SCOPUS Indexed). Jorge Tiago Martins (The University of Sheffield, UK) and Andreea Molnar (University of Portsmouth, UK) IGI Global, USA, 2016 62-109 .

Md Hasanul Ferdaus, Rizvee Hassan Prito, Masud Ahmed, Mohammad Manzurul Islam, Md Sawkat Ali, Muhammad Ibrahim and et al. : MangoImageBD: An Extensive Mango Image Dataset for Identification and Classification of Various Mango Varieties in Bangladesh, Data in Brief (impact factor:1.0) , vol.62 , no.5 Elsevier , pp.1-16 , 2025 .

Faisal Tareque Shohan, Abu Ubaida Akash, Muhammad Ibrahim and Mohammad Shafiul Alam : A Data-Driven Approach for Predicting Crime Occurrence using Machine Learning Models, International Journal of Data Science and Analytics (impact factor:3.4) , vol.20 , no.2 Springer , pp.1-22 , 2025 .

Md Tahsin, Muhammad Ibrahim, Anika Tabassum Nafisa and et al. : PaddyVarietyBD: Classifying paddy variations of Bangladesh with a novel image dataset, Data in Brief (impact factor:1.0) , vol.60 , no.1 Elsevier , pp.1 - 15 , 2025 .

Md Mehedi Hasan, Muhammad Ibrahim and Md Sawkat Ali : Selecting Update Blocks of Convolutional Neural Networks using Genetic Algorithm in Transfer Learning, Machine Graphics and Vision , vol.33 , no.3 Institute of Information Technology, Warsaw University of Life Sciences, Poland , pp.55 - 70 , 2024 .

Mohd Sayemul Haque, Md Fahim and Muhammad Ibrahim : An Exploratory Study on Simulated Annealing for Feature Selection in Learning-to-Rank, International Journal of Intelligent Systems and Applications (Scopus Indexed) , vol.16 , no.4 MECS Press, Hong Kong , pp.86-103 , 2024 .

Nafis Sajid, Md Rashidul Hasan and Muhammad Ibrahim : Feature Engineering in Learning-to-Rank for Community Question Answering Task, International Journal of Computers and Applications (Scopus Indexed) , vol.46 , no.7 Taylor and Francis, UK , pp.1-23 , 2024 .

Nazifa Hia, Ishrat Emu, Muhammad Ibrahim and Sumon Ahmed : A Differential Evolution-based Pseudotime Estimation Method for Single-cell Data, International Journal of Advanced Computer Science and Applications (SCI and Scopus Indexed), SAI Publishers, UK , vol.15 , no.6 The SAI Publishers, UK , pp.1504-1513 , 2024 .

Sarder Iftekhar Ahmed, Muhammad Ibrahim, Md Nadim, Md Mizanur Rahman, Maria Mehjabin Shejunti, Taskeed Jabid and Md Sawkat Ali : MangoLeafBD: A Comprehensive Image Dataset to Classify Diseased and Healthy Mango Leaves, Data-in-Brief , vol.47 , no.4 Elsevier Inc. , pp.1-12 , 2023 .

Afsana Mimi, Sayeda Fatema Tuj Zohura, Muhammad Ibrahim, Riddho Ridwanul Haque, Omar Farrok, Taskeed Jabid and Md Sawkat Ali : Identifying Selected Diseases of Leaves using Deep Learning and Transfer Learning Models, Machine Graphics and Vision , vol.32 , no.1 Institute of Information Technology, Warsaw University of Life Sciences, Poland , pp.55-71 , 2023 .

Tashreef Muhammad, Anika Bintee Aftab, Muhammad Ibrahim, Md. Mainul Ahsan, Maishameem Meherin Muhu, Shahidul Islam Khan and Mohammad Shafiul Alam : Transformer-Based Deep Learning Model for Stock Price Prediction: A Case Study on Bangladesh Stock Market, International Journal of Computational Intelligence and Applications (impact factor:1.3) , vol.22 , no.1 World Scientific Publishing Europe Ltd. , pp.1-24 , 2023 .

Dewan Tariq Hasan, Md Mosaddek Khan, Muhammad Ibrahim and Ibrahem Almansour : On Evaluation of Patrolling and Signalling Schemes to Prevent Poaching in Green Security Games, Intelligent Systems with Applications , vol.14 , no.1 Elsevier B.V. , pp.1-15 , 2022 .

Muhammad Ibrahim : Evolution of Random Forest from Decision Tree and Bagging: A Bias- Variance Perspective, Dhaka University Journal of Applied Science and Engineering (DUJASE) , vol.7 , no.1 Faculty of Engineering and Technology, University of Dhaka, Bangladesh , pp.66-71 , 2022 .

Muhammad Ibrahim : Understanding Bias and Variance of Learning-to-Rank Algorithms: An Empirical Framework, Applied Artificial Intelligence (impact factor:2.78) , vol.36 , no.1 Taylor and Francis, UK , pp.1-34 , 2021 .

Muhammad Ibrahim : Sampling Non-Relevant Documents of Training Sets for Learning-to-Rank Algorithms, International Journal of Machine Learning and Computing (Singapore) , vol.10 , no.3 , pp.406-415 , 2020 .

M. W. Allvi, M. Hasan, L. Rayan, M. Shahabuddin, Md. Mosaddek Khan and Muhammad Ibrahim : Feature Selection for Learning-to-Rank using Simulated Annealing, International Journal of Advanced Computer Science and Applications (SCI and Scopus Indexed), SAI Publishers, UK , vol.11 , no.3 , pp.699-706 , 2020 .

Muhammad Ibrahim : An Empirical Comparison of Random Forest-Based and Other Learning-to-Rank Algorithms, Pattern Analysis and Applications (Springer, Germany; part of Springer-Nature) (impact factor:2.31) , vol.23 , no.3 Springer Nature , pp.1133-1155 , 2020 .

Muhammad Ibrahim : Reducing Correlation of Random Forest-Based Learning-to-Rank Algorithms Using Sub-Sample Size, Computational Intelligence (Wiley Publishers, USA) (impact factor:1.4) , vol.35 , no.4 Wiley Publishers, USA , pp.774-798 , 2019 .

Muhammad Ibrahim and Mark Carman : Comparing Pointwise and Listwise Objective Functions for Random Forest Based Learning-to-Rank, ACM Transactions on Information Systems (ACM, USA) (impact factor:2.3) , vol.34 , no.4 ACM, USA , pp.1-43 , 2016 .

Muhammad Ibrahim, Nasimul Noman and Hitoshi Iba : Finding perfect and imperfect biclusters from gene expression data: A Heuristic and a meta-heuristic approach, International Journal of Applied Chemistry (SCOPUS Indexed) , vol.8 , no.3 , pp.225-242 , 2012 .

Muhammad Ibrahim and Ahsan Raja Chowdhury : An Improved Heuristic Algorithm to Minimize Complete Test Set of K-CNOT Circuits for Single and Multiple Stuck-at Fault Model, Journal of Computing , vol.4 , no.2 , pp.137-146 , 2012 .

Abu Kalam, Md Enamul Haque, Mohammad Jashem, Mahamudul Hasan, Muhammad Ibrahim and Taskeed Jabid "Facial Expression Recognition Using Local Composition Pattern." The 7th International Conference on Computer and Communications Management , pp. 63-67. Thailand: ACM, USA, 2019 .

Muhammad Ibrahim "Scalability and Performance of Random Forest based Learning-to-Rank for Information Retrieval." ACM SIGIR Forum , pp. 73-74. Japan: ACM, USA, 2017 .

Muhammad Ibrahim and Mark Carman "Undersampling Techniques to Re-balance Training Data for Large Scale Learning-to-Rank." The 10th Asia Information Retrieval Society Conference (AIRS 2014) , pp. 444-457. Malaysia: Springer International Publishing, Germany, 2014 .

Muhammad Ibrahim and Mark Carman "Improving Scalability and Performance of Random Forest Based Learning-to-Rank Algorithms by Aggressive Subsampling." The 12th Australasian Data Mining Conference (AusDM 2014) , pp. 91-99. Australia: CRPIT, Australia, 2014 .

Muhammad Ibrahim, Nasimul Noman and Hitoshi Iba "On the Complexity and Completeness of Robust Biclustering Algorithm (ROBA)." The 4th International Conference on Bioinformatics and Biomedical Engineering (ICBBE) Chengdu, China: IEEE, 2010 .

Muhammad Ibrahim, Nasimul Noman and Hitoshi Iba "Introducing Flexibility in Robust Biclustering Algorithm (ROBA) to Find Imperfect Biclusters." The 2010 International Conference on Bioinformatics and Biomedical Technology (ICBBT) (Accepted) Chengdu, China: IEEE, 2010 .

Muhammad Ibrahim, Nasimul Noman and Hitoshi Iba "Time and Space Efficient Implementation of Robust Biclustering Algorithm (ROBA)." The 20th International Conference on Genome Informatics, Posters and Software Demonstrations, 2009 , pp. 1-2. Japan: University of Tokyo, 2009 .

Muhammad Ibrahim, A. R. Chowdhury and H. M. H. Babu "On the Minimization of Complete Test Set of Reversible K-CNOT Circuits for Stuck at Fault Model." International Conference on Computer and Information Technology (ICCIT) , pp. 7-12. KUET, Khulna, Bangladesh: IEEE, 2008 .

Muhammad Ibrahim, A. R. Chowdhury and H. M. H. Babu "Minimization of Complete Test Set of k-CNOT Circuits for Single and Multiple Stuck-at Fault Model." The 23rd IEEE International Symposium on Defect and Fault tolerance in VLSI Systems, 2008, , pp. 290-298. Cambridge (MA), USA: IEEE, 2008 .