Muhammad Imran

Filtering-out poor-quality images for data preparation

Roopdeep Kaur, Gour Karmakar, Muhammad Imran

Filtering noise is a fundamental part of data preparation that enhances image quality for applications such as object segmentation, detection, and recognition. Various noise reduction techniques are proposed in the literature, including the use of median, Gaussian, and bilateral…Read more

Static Metrics Are Insufficient: Predicting Java Method Energy Usage with Execution Time

Muhammad Imran, Vincenzo Stoico, Ivano Malavolta

The increasing energy demand of software systems is raising concerns about their environmental impact and associated costs. Reasoning on energy usage early in the development flow has the potential to significantly reduce the overall energy usage of a software system, as it allow…Read more

A Comparative Framework for Political Violence Event Classification Using Machine Learning, Deep Learning, and Zero-Shot Language Models

Ujala Beenish, Saadia Ishtiaq Nauman, Sadaf Abdul Rauf, Fatima Mumtaz, Muhammad Ghulam Abbas Malik, Muhammad Imran, et al.

Political violence poses a significant challenge to global stability, underscoring the need for comparative analytical models that support analytical interpretation of structured conflict data. This paper presents a comparative evaluation of 12 machine learning approaches, includ…Read more

Geospatial Robust Wheat Yield Prediction Using Machine Learning and Integrated Crop Growth Model and Time-Series Satellite Data

Rana Ahmad Faraz Ishaq, Guanhua Zhou, Guifei Jing, Syed Roshaan Ali Shah, Aamir Ali, Muhammad Imran, et al.

Accurate crop yield modeling (CYM) is inherently challenging due to the complex, nonlinear, and temporally dynamic interactions of biotic and abiotic factors. Crop traits, which historically capture the cumulative effect of these factors, exhibit functional relationships critical…Read more

Evaluating Realistic Adversarial Attacks against Machine Learning Models for Windows PE Malware Detection

Muhammad Imran, Annalisa Appice, Donato Malerba

During the last decade, the cybersecurity literature has conferred a high-level role to machine learning as a powerful security paradigm to recognise malicious software in modern anti-malware systems. However, a non-negligible limitation of machine learning methods used to train…Read more

Thermohydraulic performance of MXene-based nanofluid-cooled micro-porous heat sink for electronics cooling: A numerical and machine learning optimization approach

Adeel Arshad, Muhammad Saeed, M. Ikhlaq, Muhammad Imran