5 citations · 10 across the 5 of their papers we have counts for
5 papers
Domain Specific Benchmarks for Evaluating Multimodal Large Language Models
Khizar Anjum, Muhammad Arbab Arshad, Kadhim Hayawi +10
Large language models (LLMs) are increasingly being deployed across disciplines due to their advanced reasoning and problem solving capabilities. To measure their effectiveness, va…
From Predictive Importance to Causality: Which Machine Learning Model Reflects Reality?
Muhammad Arbab Arshad, Pallavi Kandanur, Saurabh Sonawani +2
This study analyzes the Ames Housing Dataset using CatBoost and LightGBM models to explore feature importance and causal relationships in housing price prediction. We examine the c…
Putting GPT-4o to the Sword: A Comprehensive Evaluation of Language, Vision, Speech, and Multimodal Proficiency
Sakib Shahriar, Brady Lund, Nishith Reddy Mannuru +5
As large language models (LLMs) continue to advance, evaluating their comprehensive capabilities becomes significant for their application in various fields. This research study co…
Mutation-based Fault Localization of Deep Neural Networks
Ali Ghanbari, Deepak-George Thomas, Muhammad Arbab Arshad +1
Deep neural networks (DNNs) are susceptible to bugs, just like other types of software systems. A significant uptick in using DNN, and its applications in wide-ranging areas, inclu…
The Power Of Simplicity: Why Simple Linear Models Outperform Complex Machine Learning Techniques -- Case Of Breast Cancer Diagnosis
Muhammad Arbab Arshad, Sakib Shahriar, Khizar Anjum
This research paper investigates the effectiveness of simple linear models versus complex machine learning techniques in breast cancer diagnosis, emphasizing the importance of inte…