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Application of Hierarchical Temporal Memory Theory for Document Categorization
The current work intends to study the performance of the Hierarchical Temporal Memory(HTM) theory for automated classification of text as well as documents.
Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overview
In this paper, we propose a unifying conceptualization: the predictive bias framework for NLP.
Brain Tumor Detection Based on Bilateral Symmetry Information
Advances in computing technology have allowed researchers across many fields of endeavor to collect and maintain vast amounts of observational statistical data such as clinical data, biological patient data, data regarding access of web sites, financial data, and the like. Brain Magnetic Resonance Imaging(MRI)segmentation is a complex problem in the field of medical imaging despite various presented methods. MR image of human brain can be divided into several sub regions especially soft tissues such as gray matter, white matter and cerebrospinal fluid. Although edge information is the main clue in image segmentation, it can not get a better result in analysis the content of images without combining other information. The segmentation of brain tissue in the magnetic resonance imaging(MRI)is very important for detecting the existence and outlines of tumors. In this paper, an algorithm about segmentation based on the symmetry character of brain MRI image is presented. Our goal is to detect the position and boundary of tumors automatically. Experiments were conducted on real pictures, and the results show that the algorithm is flexible and convenient.
