University of Malaya

About: University of Malaya is a education organization based out in Kuala Lumpur, Malaysia. It is known for research contribution in the topics: Population & Fiber laser. The organization has 25087 authors who have published 51491 publications receiving 1036791 citations. The organization is also known as: UM & Universiti Malaya.

Synthesis and Characterization of Anatase Titanium Dioxide Nanoparticles Using Egg White Solution via Sol- Gel Method

Abstract: Anatase titanium dioxide nanoparticles (TNPs) are synthesized by a simple and cost-effective process with and without freshly water-soluble egg white proteins (albumin) via sol-gel method. The main advantage of using egg white proteins as a gelling agent is that it can provide long-term stability for nanoparticles by preventing particles agglomeration. The X-ray diffraction and FTIR results indicate that the synthesized nanoparticles have only the anatase structure without the presence of any other phase impurities. Additionally, the TNPs are characterized by a number of techniques such as thermogravimetric analysis (TGA), X-ray diffraction analysis (XRD), transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FTIR), and ultraviolet visible spectra (UV-vis). The sizes of titanium dioxide nanoparticles with and without using egg white solution are nm and nm, respectively. The results indicate that egg white solution is a reliable and cheap green gelling agent that can be used as a matrix in the sol-gel method to synthesis tiny size TNPs.

Automated Detection of Alzheimer’s Disease Using Brain MRI Images– A Study with Various Feature Extraction Techniques

Abstract: The aim of this work is to develop a Computer-Aided-Brain-Diagnosis (CABD) system that can determine if a brain scan shows signs of Alzheimer’s disease. The method utilizes Magnetic Resonance Imaging (MRI) for classification with several feature extraction techniques. MRI is a non-invasive procedure, widely adopted in hospitals to examine cognitive abnormalities. Images are acquired using the T2 imaging sequence. The paradigm consists of a series of quantitative techniques: filtering, feature extraction, Student’s t-test based feature selection, and k-Nearest Neighbor (KNN) based classification. Additionally, a comparative analysis is done by implementing other feature extraction procedures that are described in the literature. Our findings suggest that the Shearlet Transform (ST) feature extraction technique offers improved results for Alzheimer’s diagnosis as compared to alternative methods. The proposed CABD tool with the ST + KNN technique provided accuracy of 94.54%, precision of 88.33%, sensitivity of 96.30% and specificity of 93.64%. Furthermore, this tool also offered an accuracy, precision, sensitivity and specificity of 98.48%, 100%, 96.97% and 100%, respectively, with the benchmark MRI database.