Tariq Khan

Macquarie University, Engineering, Graduate Student

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PROFILEHighly energetic professional academic, educator, writer, presenter, computer scientist and researcher with h-index 8 and i10-index 6. Over 8 years of research experience in Digital Image Processing and Reconfigurable Electronics. Worked as a Lecturer for 2 years, Lab Engineer 2 years and 2.5 years as Honours Project Liaison Officer. Published 24 Journal Articles and 10 Conference Proceedings.

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A Semantically Flexible Feature Fusion Network for Retinal Vessel Segmentation

Communications in Computer and Information Science

The automatic detection of retinal blood vessels by computer aided techniques plays an important ... more The automatic detection of retinal blood vessels by computer aided techniques plays an important role in the diagnosis of diabetic retinopathy, glaucoma, and macular degeneration. In this paper we present a semantically flexible feature fusion network that employs residual skip connections between adjacent neurons to improve retinal vessel detection. This yields a method that can be trained employing residual learning. To illustrate the utility of our method for retinal blood vessel detection, we show results on two publicly available data sets, i.e. DRIVE and STARE. In our experimental evaluation we include widely used evaluation metrics and compare our results with those yielded by alternatives elsewhere in the literature. In our experiments, our method is quite competitive, delivering a margin of sensitivity and accuracy improvement as compared to the alternatives under consideration.

Machine Learning: Quantum vs Classical

IEEE Access

Encouraged by growing computing power and algorithmic development, machine learning technologies ... more Encouraged by growing computing power and algorithmic development, machine learning technologies have become powerful tools for a wide variety of application areas, spanning from agriculture to chemistry and natural language processing. The use of quantum systems to process classical data using machine learning algorithms has given rise to an emerging research area, i.e. quantum machine learning. Despite its origins in the processing of classical data, quantum machine learning also explores the use of quantum phenomena for learning systems, the use of quantum computers for learning on quantum data and how machine learning algorithms and software can be formulated and implemented on quantum computers. Quantum machine learning can have a transformational effect on computer science. It may speed up the processing of information well beyond the existing classical speeds. Recent work has seen the development of quantum algorithms that could serve as foundations for machine learning applications. Despite its great promise, there are still significant hardware and software challenges that need to be resolved before quantum machine learning becomes practical. In this paper, we present an overview of quantum machine learning in the light of classical approaches. Departing from foundational concepts of machine learning and quantum computing, we discuss various technical contributions, strengths and similarities of the research work in this domain. We also elaborate upon the recent progress of different quantum machine learning approaches, their complexity, and applications in various fields such as physics, chemistry and natural language processing. INDEX TERMS Quantum machine learning, quantum computing, quantum algorithms, QuBit.

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