Habib Ullah is an Associate Professor with Norwegian University of Life Sciences, Norway. He received the Ph.D. degree in information and communication technology (Computer Vision) from the University of Trento, Italy, in 2015, the M.S. degree in electronics and computer engineering from Hanyang University, South Korea, in 2009. He received his B.Sc. degree in Computer Systems Engineering from NWFP University of Engineering and Technology, Peshawar, in 2006. From 2015 to 2016, he was an Assistant Professor in electrical engineering with COMSATS University Islamabad, Pakistan. From 2016 to 2020, he was an Assistant Professor with the College of Computer Science and Engineering, University of Ha’il, Saudi Arabia. In 2020, he was a Postdoctoral Researcher with The Arctic University of Norway, Norway.
His primary research interests include computer vision and machine learning. He is involved in many PhD projects. For example, he is investigating audio classification of underwater fish feeding sounds. Monitoring fish feeding behavior in aquaculture is essential, yet environmental challenges like water turbidity limit the effectiveness of traditional visual techniques. In this project, a simple data preprocessing strategy is developed that employs stochastic frame aggregation to selectively emphasize salient features without distorting the spectral information. He is additionally involved in another project related to zero-shot learning (ZSL). The purpose is to recognize and classify objects, concepts, or tasks it has never seen before. This approach is particularly useful in scenarios where collecting labeled data for every possible class is impractical, such as rare species identification, medical diagnosis, and object recognition in dynamic environments. By transferring knowledge from seen to unseen classes, zero-shot learning expands the capabilities of AI systems, making them more adaptable, scalable, and generalizable across diverse domains. He is also partnering on a different project where advanced deep learning models are explored to recognize stressed salmon fish and fresh salmon fish by analyzing the patterns of dots on their skin. They detect variations in dot patterns that
indicate stress levels or freshness. Stressed salmon often exhibit distinct changes in skin pigmentation and dot distribution due to physiological responses, while fresh salmon maintains a consistent and healthy pattern. He is also collaborating on the controlled environment agriculture using artificial intelligence project. Growing crops indoors under exact control of environmental elements like temperature, humidity, light, and nutritional levels is known as controlled environment agriculture (CEA). The utilization of sensors, automation, and data analysis in artificial intelligence (AI) methodologies has the potential to improve CEA systems. Plants are grown indoors under artificial light, densely packed together, and stacked on multiple layers. In CEA, one can precisely control internal environmental factors such as light, temperature, humidity, CO2 level, and nutrient supply.
He has co-authored around 70 scientific publications in internationally recognized peer-reviewed journals and conference proceedings. He has more than 2,200 citations on Google Scholar, with an h-index of approximately 28, reflecting the significant impact of his research in the fields of machine learning, and computer vision. He regularly reviews scientific articles and book chapters in the fields including computer vision, machine learning, and image processing. He served as the Editor of the book Machine Learning Techniques and Sensor Applications for Human Emotion, Activity Recognition, and Support (ML-SHEARS), published by Springer. Additionally, he contributed to various prestigious academic roles, including serving as the Track Chair for Multimedia Processing at the 13th International Symposium on Information and Communication Technology and as the Guest Editor for the Special Issue of MDPI Remote Sensing Journal, titled Deep Learning Meets Remote Sensing for Earth Observation and Monitoring. He was also a Program Committee Member for the International Workshop on Designing the Conceptual Landscape for Explainable AI-Ready (XAIR) Infrastructure (DCLXVI) and the IEEE International Conference on Digital Health. Furthermore, he served as an Associate Editor for the IEEE Access journal. His extensive editorial and organizational contributions reflect his expertise and commitment to advancing research in machine learning, sensor applications, multimedia processing, and AI-driven technologies.
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