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International Journal of Hybrid Intelligence (2 papers in press)
A Brief Survey on Image Segmentation based on Quantum Inspired Neural Network by Pankaj Pal, Siddhartha Bhattacharyya, Jan Platos, Vaclav Snasel Abstract: Information retrieval is a rudimentary approach and is highly solicited and recognized from image analysis for soft computing research field. In the field of classical approach, image processing task is predictable to recover the objects from the noisy image. Quantum computation plays a vital role to recover the object from binary, gray, pure or true color images using quantum computation implementation endorsement from its superposition principle. In this review paper sigmoidal activation function and multilevel sigmoidal activation function is used to fulfil the objective for recovering the object using either denoising or segmentation technique. Neuro-biological network architecture comprises different nodes corresponding to the pixels are converted to the qubit neurons and has the ability for information retrieval capability from the objects by means of qubit neurons in phase manner. In this review paper authors present the brief survey on the image processing trend over image denoising as well as image segmentation scenario. Keywords: Classical approach; Image segmentation; Sigmoidal activation function; Multilevel sigmoidal activation function; Neuro-biological network.
Quantitative prognostic factor extraction of epidemic
thrombosis using machine learning strategy by Tianle Zhou, Danni Deng, Chaoyi Chu, Jie Cao Abstract: In recent years, artificial intelligence and machine learning have become increasingly involved in the treatment of prevalent human diseases. Acute ischemic stroke (AIS) is an increasingly severe disease with a high risk of thrombosis resulting in loss of neurological function or death. MT with mechanical thrombectomy has become the mainstream treatment. Apart from the common factors such as blood glucose, NIHSS, and blood pressure level, etc., there are still unknown factors may have influence for prognosis after MT surgery. In this study, with the help of machine learning strategy, high-dimensional data of patients are mined, and the AIS prognostic prediction model is established in order to quantify the key influencing factors and determine the relationship between these parameters and the prognosis. This study is supposed to provide a set of methodology to evaluate the prognosis effectively. Keywords: acute ischemic stroke; AIS; mechanical thrombectomy; prognosis factor extraction; high-dimensional data; machine learning. DOI: 10.1504/IJHI.2020.10031627