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Published Works
IBA faculty co-authors research paper on integrated prediction system for a retail petrol station

Dr. Tahir Syed, Assistant Professor, Department of Computer Science, has co-authored a paper titled, 'Real-time forecasting of petrol retail using dilated causal CNNs'. The research paper has been published in the high-impact HEC-recognized Journal of Ambient Intelligence and Humanized Computing, with a W-ranking.

Dr. Tahir has worked extensively with fundamental Machine Learning problems such as class imbalance and distribution drift and specializes in designing new losses for optimizing neural networks for specific needs. He was nominated to design Pakistan's first graduate Data Science curriculum and in 2020, when the IBA Karachi began the said program, he joined the IBA Karachi family.

Abstract

The recent popularity of smart cities and smart homes has made the adoption of Internet of Things (IoT) devices ubiquitous. Most of these IoT devices are low-end devices with limited capabilities. For neural network based predictive models, the low processing power of connected things is a limitation when training them. In addition, it is still a common practice to deploy these models on cloud servers that possess dedicated high performance computing hardware. However, for IoT applications, it is not feasible to send voluminous raw data to the cloud or a remote backend server on account of high latency, information security concerns or lack of network coverage. In this work, we develop an integrated prediction system for a retail petrol station within the operational constraints of the IoT ecosystem. Our main contribution is the combination of the recent concepts of dilated convolution and the so-called causal convolution into the 1D dilated causal convolutional neural network for time-series prediction.

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Published Works
IBA faculty co-authors research paper on emerging Big Data challenges.

Dr. Tahir Syed, Assistant Professor – Department of Computer Science, has co-authored a paper titled, "Potential Deep Learning Solutions to Persistent and Emerging Big Data Challenges—A Practitioners' Cookbook". The research paper has been published in the high-impact HEC-recognized ACM Computing Surveys journal, with a W-ranking.

Dr. Tahir has worked extensively with fundamental Machine Learning problems such as class imbalance and distribution drift, and specializes in designing new losses for optimizing neural networks for specific needs. He was nominated to design Pakistan's first graduate Data Science curriculum and in 2020, when the IBA Karachi began the said program, he joined the IBA Karachi family.

Abstract:

The phenomenon of Big Data continues to present moving targets for the scientific and technological state-of-the-art. This work demonstrates that the solution space of these challenges has expanded with deep learning now moving beyond traditional applications in computer vision and natural language processing to diverse and core machine learning tasks such as learning with streaming and non-iid-data, partial supervision, and large volumes of distributed data while preserving privacy. We present a framework coalescing multiple deep methods and corresponding models as responses to specific Big Data challenges. First, we perform a detailed per-challenge review of existing techniques, with benchmarks and usage advice, and subsequently synthesize them together into one organic construct that we discover principally uses extensions of one underlying model, the auto-encoder. This work therefore provides a synthesis where challenges at scale across the Vs of Big Data could be addressed by new algorithms and architectures being proposed in the deep learning community. The value being proposed to the reader from either community in terms of nomenclature, concepts, and techniques of the other would advance the cause of multi-disciplinary, transversal research and accelerate the advance of technology in both domains.

For more information: https://dl.acm.org/doi/abs/10.1145/3427476